In order to evaluate the effectiveness of our proposed method, we compared it with 21 state-of-the-art clustering approaches on five datasets listed in Table REF . As shown in Table REF , different from most prior methods training and evaluating on the whole datasets on the top corner, we train and evaluate SCAN, NNM and SIC by using the train and val split respectively like SCAN [1]}, which allows us to study the generalization properties of our method for novel unseen examples. The clustering results of six methods, i.e., SC [2]}, NMF [3]}, AE [4]}, DAE [5]}, DCGAN [6]} and VAE [7]}, are obtained
via \(k\) -means.
